Streaming Media’s Environmental Impact
Bibliographic record
Abstract
This group of articles, which arose from a panel planned for the 2020 annual meeting of members of the Society for Cinema and Media Studies, draws attention to an unpopular but inescapable issue: the adverse environmental effects of streaming media. Four of these brief interventions focus on streaming media’s carbon footprint, estimated by some to be 1 percent of global greenhouse gas emissions (The Shift Project 2019). This startling figure is rising at a calamitous rate as more people around the world stream more media at higher bandwidth—now exacerbated by the COVID-19 pandemic. Another factor in streaming media’s environmental impact is even less welcome: the deleterious effects of higher levels of electromagnetic frequencies that media corporations’ turn to fifth-generation (5G) wireless technology would exacerbate. These effects are well documented yet almost universally ignored. Despite all these findings, the notion abides that digital media are immaterial. Laura U. Marks introduces the research challenges involved in calculating the carbon footprint of streaming media and suggests actions consumers and media makers can take to mitigate this environmental threat. Joseph Clark discusses the implications of digitizing huge amounts of archival film and connects material histories of news film production, distribution, and preservation or disposal to contemporary issues of digital storage, streaming, and energy use, using the newsreel archive as a case study. Jason Livingston’s contribution expands on his droll and disturbing video lecture, which presents a speculative app for mobile phones that tracks streaming, correlates it to energy use and CO 2 emissions, and suggests methods to mitigate usage. Denise Oleksijczuk introduces scientific research on the health and environmental impacts of high levels of electromagnetic frequencies and suggests ways, including creative practice, to break through the resistance to these findings among telecommunications companies, governments, and the public. Lucas Hilderbrand focuses on best practices in teaching: how to educate our students about these impacts, and how teachers can resist increasing pressures to use streaming-based pedagogical media. Many communities around the world already rely on low-tech media, of necessity, and are often extremely innovative in their use (Marks 2017). However, network and media corporations are aggressively marketing devices and streaming platforms in both “developed” and “developing” regions (Cisco 2020). Many of the latter regions depend on fossil fuels and cannot afford to prioritize renewable energy and efficient systems. Thus streaming media’s carbon footprint is not just a First World problem.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; both teacher heads agree on what is shown here.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".